Future of Manufacturing Project

The Future of Manufacturing Project has become the premier platform for exploring the transformative forces shaping the manufacturing industry. At the 2024 event, David R. Brousell, MLC’s Founder, Vice President and Executive Director, delivered a powerful opening address that set the tone for the discussions and presentations to come. In his speech, Brousell examined the meteoric rise of AI technologies like ChatGPT, the growing global competition for AI leadership, and the profound implications for manufacturers. He offered data-driven insights, candid reflections on challenges, and practical guidance for navigating the AI-driven future. Below is a recording and the full text of his address — an essential read for anyone seeking to understand the opportunities and responsibilities that lie ahead for manufacturing and beyond.

Good morning, everyone, and welcome to the fourth annual Future of Manufacturing Project event.

Two years ago in Nashville, when MLC first devoted this event to examine the potential as well as challenges with artificial intelligence in manufacturing operations, OpenAI had just released the generative AI tool ChatGPT.

I had mentioned OpenAI in my talk that day, but little did we know what would soon be coming. ChatGPT took off like a rocket ship, capturing the imagination of the public, businesses, and governments, raising both aspirations as well as fears about the technology.

ChatGPT reached 100 million users in just two months after its launch, becoming the fastest growing consumer application in history at that time. Today, ChatGPT has a reported 200 million users.

ChatGPT became an inflection point for AI in general. The energy and momentum around AI in its many forms – IBM alone has identified seven types of AI — have not only continued but appear to be picking up even more steam. In the generative AI market alone, a flood of competitors has joined ChatGPT including Midjourney, Stable Diffusion, Bard, and, of course, Copilot, and others.

The amount of literature being produced about AI – and I use the term “literature” with an asterisk attached – is almost impossible to keep up with. Every day, it seems, there are multiple newspaper and magazine articles, research reports, case studies, government reports, product and service announcements, and conferences extolling both the virtues and warning of the challenges with the technology.

Huge sums of money are flowing into the AI market as nations earmark funds, announce national strategies, and set up organizations to capitalize on the technology; as technology and service providers produce new AI and augmented products and offer implementation methods; and as businesses assess the effect of AI on their competitive posture and develop their own goals and objectives with the technology.

As I said in Nashville two years ago, a worldwide competition for dominance in AI is underway – and it appears to be intensifying.

Here are a few datapoints to consider:

• In a report published in September, International Data Corp. predicted that AI would have a cumulative global economic impact of $19.9 trillion through 2030, representing 3.5% of global GDP by that year. Every new dollar spent on business-related AI solutions and services will generate, IDC estimated, $4.60 into the global economy, in terms of indirect and induced effects.
• Meanwhile, spending on AI is growing at double-digit rates. IDC estimated that worldwide spending on AI, including AI-enabled applications, infrastructure, and related IT and business services, will grow at a 29% compound annual growth rate between this year and 2028, reaching $632 billion in 2028. In terms of use cases, Smart Factory floor investments are expected to grow at a 32.5% CAGR in that time period.
• The number of AI patents granted worldwide rose 62.7% between 2021 and 2023, according to the 2024 Stanford AI Index Report. In 2022, China led global AI patent origins with a 61.1% share, significantly outpacing the U.S., which accounted for 20.9% of AI patent origins. In that same year, however, the U.S. far exceeded China in terms of financial investments. In the U.S., AI investments reached $67.2 billion in 2022, 8.7 times more than China.
• Announcing AI strategies has become commonplace among nation states. The U.S. has its National AI Initiative Act, a Presidential Executive Order on AI, and is in the process of standing up an AI Institute under the America Makes program.
• In China, well, we know there is much activity but getting reliable data is a challenge. For example, International Data Corp. estimates that China’s investment in AI will reach $38 billion in 2027. Meanwhile, a report in Yahoo Finance in September said that China’s AI industry could invest $1.4 trillion into developing the technology in the next six years. Pass the salt!
• On the other side of the world, the European Union has its AI Innovation Strategy, which calls for the creation of AI Factories across the European Union. Back in Asia, India has its AI Mission initiative; Japan’s Ministry of Economy, Trade and Industry has allocated $740 million to subsidize the AI computing industry in Japan; and South Korea has a plan to spend $6.9 billion by 2027 on artificial neural processing units and high bandwidth memory chips.

But one of the more notable announcements this year came from Saudi Arabia, which created a $100 billion fund to invest in AI and other technologies.

***

Now, cautious observers, particularly those with a historical context, might say that all the posturing and the financial projections and investment numbers merely reflect an inflated state of expectations, a euphoria born of excitement about the shiny new car. We are in what Gartner calls a hype cycle that will, in time, deflate as the challenges of implementing and optimizing use of the technology become better understood and experienced.

No doubt there is some truth in this. As we know from the experiences we’ve had with other technologies, strategic, organizational, cultural, and human behavioral challenges govern whether a technology project will ultimately be successful. I myself have talked for many years about what I call The Absorption Gap – the time and effort required to go from implementation to benefit. It often takes much longer to get to benefit than we think at the outset.

Could history repeat itself with AI? Could we yet experience a third AI winter, after the two that occurred last century when expectations were disappointed?

Anything is possible, of course, but I wouldn’t bet on it. The foundational conditions that were lacking years ago to enable AI to take off – compute power, storage, communications bandwidth, sufficiently large data sets – have all been addressed. Moore’s Law will ensure that price/performance will improve, perhaps significantly, over time. And the unrelenting need to find new competitive advantage will compel companies to go forward into the known and, perhaps more importantly, the unknown.

My friends, the AI train is leaving the station – and you must be on it.

***

The encouraging news is that aspirations with AI among manufacturers are strong and there is already some evidence that AI is, as they say, worth the squeeze.

Recent MLC research shines a light on what manufacturers are thinking about the use of AI in their operations.

Among the many findings of our new AI study, results of which were published in the Manufacturing Leadership Journal in August, perhaps the most important is that a majority of manufacturers, 55%, see AI as a “game-changer” in the industry by 2030, even though only 5% at this point in time assess their maturity level with AI in operations as “advanced”.

Perhaps propelled by this perception, 78% said they plan to increase spending overall on AI tools over the next 12 to 24 months, with

one-fifth expecting to increase their spending by more than 30%. Already, more than half are using GenAI tools such as ChatGPT or Microsoft’s Copilot, and more than 80% said they expect to increase the use of such tools in the next two years.

As manufacturers ramp up their AI investments, early indications about the impact and return on these investments are looking positive.

In July, EY, a member of the Manufacturing Leadership Council and a partner in the Future of Manufacturing Project, published the results of its first AI Pulse survey, which asked 500 senior leaders across industries in the U.S. about AI investments, impacts, and challenges.

The study found that senior leaders whose organizations are investing in AI have seen tangible results across business functions, including about three-quarters who are already experiencing positive ROI in operational efficiencies, employee productivity, and customer satisfaction.

Moreover, the EY study found that organizations that are committing 5% or more of their budgets on AI are seeing “outsized returns.”

And all of this is happening as AI continues to advance. This year’s Stanford report assessed that AI has now surpassed human performance on several benchmarks, including in image classification, visual reasoning, and English understanding. However, the technology still lags human capabilities in competition-level mathematics, visual common-sense reasoning, and planning, Stanford said.

I suppose that we can all take some comfort in those findings. We humans will continue to dominate – at least for the moment. We don’t know how fast AI will evolve and how capable and powerful it will become in the years ahead, but we know one thing for sure – with the amount of capital flowing into the AI market and the amount of effort being applied to its development, AI will not sit still.

As it continues to move, manufacturers have a host of issues to deal with to ensure that AI is used effectively to advance their organizations’ business and operational goals, and in a responsible manner.

Once again, MLC research spotlights some of the challenges.

The top concern, say 68%, is around data – quality, contextualization, and validation. More than 40% also identify AI skills and AI business case development as issues. Sixty-six percent say they do not have a specific set of metrics to measure the effectiveness and impact of AI implementations at this time.

This all goes back to that single digit finding I mentioned earlier about maturity levels with AI. The question is: what will it take to accelerate adoption and use of AI in operations and enable companies to climb up the maturity curve?

The easy answer is time, money, and persistence. But there’s a bit more to it than these three foundational elements. Please allow me to offer a few recommendations:

First, I can’t emphasize strongly enough about having a vision for what you want to do with AI. That may sound obvious, and it is on one level, but it takes a lot of thinking, strategizing and even soul-searching to craft an idea of what your company could be in 10 or 15 or 20 years.

So, think big about AI and what your company might be able to do with it. Think about the art of the possible. Think about growth and not just in what you are doing now but what you could be doing that’s new, innovative, and exciting.

At first, you will turn your attention to using AI to become more efficient, more cost-effective, faster. These are all important objectives, but don’t stop there. Imagine a better future.

And once you craft a business vision for the future – a vision that should be flexible enough to change when needed – make sure your AI strategy “functions as the fuel,” as Council member Deloitte says, for the business strategy.

Second, keep up with the evolution of the technology. There is a lot of energy and momentum around AI, as I have detailed, and new developments are occurring frequently. The technology must be monitored and understood, as well as the rules, laws, and regulations that will govern its use. Designate someone or a specific business unit in your company to keep track of developments to understand their potential to help your business.

Third, don’t try to go it alone. Plug into the community. MLC is here to bring together your fellow manufacturers, technology and services providers, academics and research organizations, and sister groups such as the IRI, CESMII, and MxD so you have access to a robust community for help.

Fourth, work on the metrics. Experimentation with AI is necessary and important, but the laws of business must be served. Find, adopt, and use ROI models to prove return so that investment continues.

Fifth, it is essential to take a human-centered approach to AI. Bring people along by showing them the benefits and being candid with them about the challenges. Change management and training are key.

Taken together, these simple, common-sense tenets can help you navigate your journey with AI.

***

What we do here today, and what you do after this conference when you go back to your companies, will help propel the manufacturing industry forward.

But our responsibility is greater than just our own industry. AI has not only the potential to reshape manufacturing but society itself. As it has many times throughout history, this industry is playing a vital role in deciding what the future might look like. Once again, the mantle of change lies at our feet.

I would like to close with a quote from a book called “The Age of AI And Our Human Future,” authored by Henry Kissinger, Eric Schmidt and Daniel Huttenlocher:

“The advent of AI, with its capacity to learn and process information in ways that human reason alone cannot, may yield progress on questions that have proven beyond our capacity to answer. But success will produce new questions … Human intelligence and artificial intelligence are meeting, being applied to pursuits on national, continental, and even global scales.

“Understanding this transition, and developing a guiding ethic for it, will require commitment and insight from many elements of society: scientists and strategists, statesmen and philosophers, clerics and CEOs. This commitment must be made within nations and among them. Now is the time to define both our partnership with AI and the reality that will result.”  

Thank you and have a great conference.

About the author:
David R. Brousell is the Founder, Vice President and Executive Director, Manufacturing Leadership Council

Photos by David Bohrer / National Assoc. of Manufacturers

Digital transformation is today’s tested and proven path to business resilience. We help manufacturers digitally transform to strengthen their resilience every day. But our era is defined less by permanence than by alacrity of adaptation. Manufacturers must already evolve their approach to business resilience to remain competitive.

Granted, the need for this evolution is neither intuitive nor obvious. And in fairness, resilient manufacturing enterprises in 2030 will look much like such enterprises do today. But the means for achieving and sustaining resilience by decade’s end will be radically different. Here’s why, and how to prepare for it.

Why business resilience will be different in 2030

Resilience describes a business’s ability to adapt to change effectively and efficiently. For many reasons, data-driven manufacturers are indeed able to adapt well. That’s why businesses that aren’t already data-driven are becoming so.

But digital transformation is a continuum. And few companies believe they have done all they need to do in their transformation. Most recognize that they are in the early or intermediate stages of digital transformation. That will change.

By 2030, businesses that lead their industry will have passed beyond the stages of digital transformation. The businesses will have entered a state of digital dexterity. In that state, business resilience manifests in sharply different ways than it does today.

How business resilience will be different in 2030

Business resilience based on digital dexterity has many facets. Among them, three stand out as being potentially unexpected. First, high-level objectives will remain vital to defining initiatives aimed at strengthening business resilience. But those objectives will not be the exclusive driver. Digitally dexterous manufacturers will employ a fluid governance model. This reflects the future reality wherein company-wide resilience initiatives will emerge from diverse organizational levels, and not always from the top.

A second difference in the way business resilience will manifest lies with the workforce. Digital dexterity means making sure that the workforce has access to, and understands how to use, your business’s data and technologies. Employees will have the information they need to understand how changing their own workflows could have wide-reaching benefits. They will be able to see ways to boost resilience from their specific position. These will be ways not obvious from a top-down view.

In fact, the top-down view will itself be different, which is the third key facet we are considering. Digital dexterity will mean that all executives – not just those in IT – feel at home with technology. In the same way that an executive today can assess how well one staff member is performing, they will be able to assess how well a technology is performing. For example, is a particular AI algorithm doing what it should, and is it enabling the desired business resilience outcomes? That level of digital literacy will be a basic requirement for leaders in digitally dexterous companies.

What you can do now to prepare for the future of business resilience

We’ve called out ways of enabling business resilience in the future that will be strikingly different. Preparing for those differences will set you on the right path. A cultural awareness and acceptance of shared responsibility for business resilience takes time to develop. But it also follows naturally as your workforce evolves on the path to digital dexterity. The key here is to ensure that everyone in your organization has the opportunity to participate in the digitization of their roles. Organizational change management will be instrumental in this workforce development.

Establishing a higher baseline of digital literacy among your leadership team takes time as well, and you’ll need to begin soon. Leaders must be able to evaluate technologies for their effectiveness as intuitively as they do employees, but that is predicated on educating and training leaders with the requisite skills. Ideally, upskilling and reskilling should always remain slightly ahead of technological change.

Business resilience in 2030 will be decentralized and deeply integrated in the manufacturing enterprises’ people, processes and technologies. It will be the inevitable outcome of your organization’s digital dexterity.

To learn more, please visit NTT DATA’s manufacturing page.

About the Author
Siva Gurupackiam is Senior Vice President of Manufacturing Industry Solutions at NTT DATA.

To build the right foundation for long-term growth, manufacturers must put innovation and digital strategies at the heart of transformation. 

TAKEAWAYS:
● Massive geopolitical, technological and cultural changes have prompted unprecedented shifts in industrial manufacturing.
● As pressures mount to build smart products, digitize operations and exceed customer expectations, incremental approaches to change are falling short.
● Manufacturers need to design a transformation that is innovative, agile, cross-functional, and scalable to realize tangible business value.  

Over the last several years, challenges related to geopolitics, technology, the pandemic and climate change have shaken the foundation of industrial manufacturing – creating tectonic shifts in how manufacturers think, operate and deliver their products and services.

The pressure to make products smart and connected, digitize the factory and operations, create more automated and reliable supply chains, and deliver on rising customer expectations presents significant opportunities to move toward radical growth. Yet many manufacturers’ current approaches – driven by siloed teams, functions rather than strategies, and static views of competitive and operational landscapes – present significant risks on the road to reinvention.

Legacy manufacturers trying to speed ahead into the future of smart products, digital platforms and new service-oriented business models urgently need a better way forward. A hybrid, innovation-led approach that incubates future businesses while simultaneously optimizing today’s products and operations can provide practical, value-driven solutions in both the short and long term.

Disruptive Forces Shift Operational Priorities

The range of disruptive forces manufacturers are experiencing has implications across the entire enterprise. These forces include:

• sector convergence
• power shifts within value chains
• evolving customer expectations
• volatile macroeconomic environments
• tougher workforce dynamics
• sustainability pressures
• transformative technologies

In response, manufacturers are prioritizing digital transformation and innovation-related investments. According to the EY January 2023 CEO Outlook Pulse survey, virtually all (97%) of industrial manufacturing CEOs indicated that continuing digital and technology transformation to deliver growth and operational advantages is either a very or fairly important near-term priority, despite near-term economic uncertainty.

Through these investments, manufacturers seek to revolutionize processes by implementing innovations such as digital twins, artificial intelligence and machine learning. At the same time, they are advancing product and service offerings across the value chain that are smart and connected.

However, to fully realize the significant opportunities arising from disruption and the value of these investments, manufacturers must tie them to a unifying vision of where their markets are headed.

“Legacy manufacturers trying to speed ahead into the future of smart products, digital platforms and new service-oriented business models urgently need a better way forward”

A Different, Innovation-Focused Approach to Manufacturing Transformation

Forging a new path to the future will require manufacturers to put their growth agenda at the center of transformation. This plan needs to be backed by a detailed strategic roadmap that accounts for both the innovations needed to succeed and the transformation required to launch and scale these breakthroughs in the market. It must also be developed with an enterprise-wide outcome in mind, breaking down functional and geographic silos.

While not every initiative needs extensive cross-organizational coordination and buy-in, many of the most prominent areas for manufacturers’ business reinvention are inherently interdisciplinary. Effective transformations demand engagement with a range of intersecting value drivers, including product and service innovation; customer experience; intelligent and sustainable supply chains; workforce and talent; and business model innovation.

Image 1: The value drivers of tomorrow

Image 1: A chart showing the value drivers of tomorrow that intersect include product and service innovation; customer experience; intelligent and sustainable supply chains; workforce and talent; and business model innovation. Source: EY

Once a decision is made to pursue small- or large-scale reinvention, manufacturers can maximize the speed, agility and long-term value generation by adopting the following best practices.

Start With the End in Mind But Also Know Where to Begin

With business challenges clearly defined, manufacturers should explore solutions using a future-back approach that starts with the end in mind. The critical first step involves rapidly defining and assessing tomorrow’s potential futures from the outside-in, and predicting how these future scenarios may shape customer needs, market conditions and value pools across time horizons. In parallel, organizations must also perform a thorough review of their current capabilities, particularly those that may play an increasingly important role in future competitiveness.

Taken together, assessments of a company’s current state and potential futures serve as the foundation of a new strategy that will act as the transformation’s North Star – guiding it relative to the level of ambition, while optimizing development speed and investment spending. This strategy should also provide a framework for cross-enterprise transparency and engagement.

Image 2: Our approach to transformation

Image 2: A chart showing the continuous innovation process which defines the future growth agenda, designs the transformation plan to deliver results both at the enterprise and functional level. Source: EY

Design the Transformation With a Hands-On Approach to Innovation

Rapid prototyping is an essential step for manufacturers to substantiate their future views and refine perspectives on must-have innovations. Multiple, accelerated iterations of both physical products and digital offerings are important for assessing features and costs in the context of various customer expectations, demand levels and competition scenarios.

Making the future tangible will pay dividends in the design phase of the transformation. Understanding necessary enterprise-wide adaptations – from specific technology or talent enhancements to new business models – will inform an achievable, cross-functional plan. The ability to “show your work” will also help build support across various internal constituencies as they are enlisted in the effort.

Start with the end in mind

Image 3: A chart showing the continuous innovation process which defines the future growth agenda, designs the transformation plan, analyzes existing products and operations, connects new products, embeds digital platforms to redesign As-A-Service (XaaS). Source: EY

Accelerate Results By Incubating New Business Model Concepts

When future growth depends on technologies, customer expectations or new value pools that are not addressed by a manufacturer’s current core competencies, innovation at the business model level may be a solution. For example, what are the implications of collecting, analyzing and monetizing data from a new connected product? Successfully assessing and addressing these impacts could enable manufacturers to leapfrog the competition.

“To fully realize the significant opportunities arising from disruption and the value of these investments, manufacturers must tie them to a unifying vision of where their markets are headed”

Given the enterprise-wide implications of such changes, legacy manufacturers should prioritize small-scale experimentation with a lean, internal start-up style approach. In conjunction with ongoing physical product innovation, manufacturers will want to invest in teams to explore the internal and external infrastructure and capabilities needed to support an operating model for an offering’s full lifecycle. This approach can be particularly beneficial for evaluating the technology infrastructure required for a potential new business model. By using focused, lean teams, manufacturers can leverage agile sprints to further define capabilities and architectural requirements as the transformation progresses.

Identify Ecosystem Partners to Address Critical Capability Gaps

As manufacturers innovate, they will have to decide whether their long-term strategies are better served by seeking ecosystem partners versus internally developing or acquiring new capabilities. Assessing targeted value pools and key differentiators of success can help inform these decisions.

Manufacturers whose future offerings are likely to depend on the secure sharing and analysis of data at scale will want to consider partnering with technology providers as a more efficient path to market. However, the factors to consider when choosing a mission-critical partner can be extensive, particularly given the high cost of potential failure. Legacy manufacturers can benefit from a structured process to weigh the benefits of various collaborators.

Manufacturers exploring business model innovation may also find a need for ecosystem partners outside of the technology sector. For example, future mobility business models may involve close collaboration among vehicle OEMs, energy, infrastructure and insurance firms.

Key Questions For Manufacturers Considering Reinvention

The urgency around transformation has escalated as manufacturers grapple with multi-faceted disruptions. Reactive, short-term focused responses limit the vision for enterprise-wide transformation. When thinking about the right approach, manufacturers should consider the following questions:

1. Product and service innovation: How do we evolve our product portfolio to create dynamic products and services that address customer demands for smarter and more connected features, customization, and safety/security?
2. Customer experience: How does a shift from B2B to B2C or D2C impact how we operate today?
3. Operations:
   a. How can we leverage emerging technologies to future-proof our supply chain?
   b. How do we design our future manufacturing capabilities as a competitive differentiator?
4. Workforce and talent: How can we advance innovation as a core capability through hiring, developing, training and incentivizing our employees?
5. Business model innovation: How do we innovate to drive the core business to peak profitability while simultaneously incubating new growth engines?

Answers to these questions may help leaders address strategic or operational gaps. With a clear vision of their organization’s future ambitions in mind, and a strong strategy for innovation-focused transformation, leaders can position their organizations to accelerate growth and leapfrog their competition.  M

About the Authors:

David Takeuchi is EY’s Global Strategy and Transformation Business Model Innovation Leader

Jerry Gootee is EY’s Global Advanced Manufacturing Sector Leader

Claudio Knizek is EY-Parthenon’s Global Advanced Manufacturing and Mobility Leader

What the past tells us about an AI and ML enriched future 

TAKEAWAYS:
● Manufacturers can leverage AI/ML strategies to overcome the prohibitive cost related to data-driven decision-making.
● AI and ML are business investments not technology investments.
● In the future, AI will drive people, process and technology changes that deliver manufacturing ROI.   

Past is prologue when it comes to AI

More than two and a half decades ago the world was in a state of panic and desperation over technology. The situation was simple: business solutions that ran supply chain, financial, and payroll systems – to name a few – could have a basic flaw in how dates were stored, and on January 1, 2000, they could simply stop working and business would stop working. The Y2K problem led to a massive investment in IT spending, turbo charging the technology capabilities that brought about the recent advancements in artificial intelligence and machine learning (AI/ML).

Then as it is now, we have a disruptive concept that threatens to upend business as usual, and executives can no longer avoid the hard discussions on how to prioritize technology investments. Then as it is now, we have a juxtaposition of business and technology interests intersecting a common goal of relevance and sustainability. The difference is that with artificial intelligence the fear of missing out is driven by excitement and opportunity.

“The difference between artificial intelligence and the Y2K problem is that with AI the fear of missing out is driven by excitement and opportunity”

The concept of data-driven decision-making, which is the basis of leveraging AI/ML in manufacturing, is not new but it has been relatively unattainable due to costs. Manufacturing is a people-driven enterprise that needs to maximize efficiencies to keep costs down and react to customer demands. The challenge is how to balance business operations with the disruption in leveraging new systems and process, balancing the potential skill gaps in using automated systems, and driving towards measurable business outcomes. As we move toward the year 2030, AI/ML can be the unifier of all those goals if manufacturers can map executive goals and business outcomes to people, process, and technology.

People power to drive top-down transformation

With the popularity of topics around generative AI and ability to access technology infrastructure for various back-office automation tasks there is urgency to innovate to stay ahead. The change that occurred since last year is driven by awareness, access, and actionable insights in the manufacturing space driving pragmatic business innovations. Executives have greater awareness of how to leverage AI/ML in business operations and are empowering their teams to invest in digital strategies around automation and efficiency. IT and business leaders within organizations are finding common cause in terms of goals for improving supply chains, understanding customer behavior, improving product quality, and investing in sustainable practices. The general concerns and narrative that AI may replace people is shifting to how AI is helping teach the workforce and automate complex production processes. While common misconceptions around AI sophistication and the practicality of certain use cases still exist, the urgency of change is overcoming the stagnant comfort of the predictable. As we move towards 2030, we will see more broad adoption of these technologies to work side-by-side and seamlessly in performing business actions severely reducing manual repetitive tasks.

Pragmatic solutions to drive maximum ROI

Concepts like Industry 4.0 drive business goals towards automated dark factories heavily leveraging IoT sensors that constantly improving plant output. Looking beyond factory operations, AI/ML solutions can optimize entire value chains as illustrated in a sample view of an equipment as a service manufacturer/distributer.

Figure 1: Leverage AI/ML solutions to drive enterprise value in manufacturing and after-market service.

The benefits earned from leveraging AI/ML solutions to track asset management and performance, labor, and financial planning are an example of data-driven decision-making and leveraging “one version of the truth” in business operations with clear ROI.

Considering these use cases can be setup very quickly and incrementally, they can drive manufacturers’ changes and urgency to adopt measurable business innovations.

Investment in business versus technology

Manufactures, like other businesses, are loathe to spend money on IT for the sake of re-platforming the business. Perception of IT spending as a costly task changes significantly with AI/ML-powered solutions.

The target goals for these solutions are business productivity, plant scrap reduction, financial visibility, and improved revenue, which are markedly more impactful than purchasing a new business application. Other tangible business applications are better customer and employee experience driven by personalized and contextual actions. The implicit benefits of cloud-based architecture and improved cyber security are attractive but not as much compared to improved margins and responsive business operations.  Focusing on technology as a means for better business results is a big difference in driving future investments in the next several years. Manufacturers will invest in approaches that drive better, transparent, explainable, and faster time to value that provide seamless decisions between different business systems with more emphasis on action versus interaction. Focus will be on how automated decision-making results in improved productivity versus the current focus on user experience and complex manual tasks.

Bringing digital revolution 2.0 into focus

As manufacturers begin adopting data-driven decision-making tools, the current AI/ML strategies will change to adapt to more complex ecosystems. While we already have focus on sustainability and automated decision-making, the future will bring more emphasis on circular economy value chains involving leveraging data across different companies across the product lifecycle and responding proactively to customer needs. By 2030, this will open up an avenue to track products, customer use, recycling, and maintenance, which will expand business opportunities for new product development and new manufacturing processes. This will ultimately and hopefully lead to digital revolution 2.0!  M

About the author:
Sandeep Anand is Senior Director of Decision Analytics and Science Platform at Infor. He has over 15 years of experience building and delivering AI/ML solutions. Experience includes solutions around yield/scrap, supply chain improvements, and smart asset management strategies. He leads the AI/ML practice at Infor, a leading enterprise cloud solutions provider.

Extrapolating from the results of NTT DATA’s recent research report, Innovation Index: Shifting from Disruption to Growth, John Petrusick, Managing Director, Manufacturing — Data and Analytics Practice, offers insights into the future of data in the latest in MLC’s Manufacturing in 2030: Crystal Ball series.

Data-Driven Transformation and the Future of Human-Centric Innovation by 2030

By 2030, I envision a significant change where data flows freely from sources including the shop floor, enterprise resource planning systems and third-party external providers. Manufacturers will centralize, harmonize and analyze data to enable real-time action. This encompasses all actions, including automated actions, to maximize business efficiency. Doing so will impact aspects of the business ranging from daily production operations on the plant floor, to engaging customers in selling activities, to the development and testing of new products.

With respect to sales, products and services, manufacturers will create seamless customer experiences in which customers can purchase goods using their preferred interface. Infusing data into a design thinking-led approach will also improve overall experiences once the product is in use. From an operational data perspective, employees on the shop floor or within the business will see performance data in real time and act instantly.

Highly commoditized or repetitious manufacturing environments might function as a largely ‘headless’ or ‘dark’ operating environment. Such environments rely on processes that are highly routine, reproducible and repeatable. They will, therefore, be able to leverage physical robots in the ecosystem to take the place of today’s manual interventions. These environments will have a significant advantage in productivity and efficiency while improving safety. They may use technologies including robotic process automation for monitoring and control, generative AI for requests from outside observers, and physical robots to take actions when physical interventions are required. Nevertheless, I do not foresee a near-term scenario in which manufacturing is 100% automated with no human involvement. Through 2030 there will continue to be entire plants, production lines and core processes where the need for creativity will keep humans integral.

Empowering Manufacturing through Data-Driven Insights

Data’s role will evolve naturally, and part of the role will entail solving problems. But the primary function data will serve is to enable insights that guide the actions of individuals, software or machinery so that they deliver increasing business value.

Currently, the data sphere focuses on providing high-quality, reliable data to manufacturing business actors — humans, software, physical robots and so on. These actors need specific analytics or insights in forms such as real-time or near-real-time Key Performance Indicators from a production line on quality to make value-adding decisions. When such data indicates deteriorating quality, we may need to halt the line and perform a root cause analysis to remediate the issue to re-gain performance with thresholds.

In a preventive maintenance scenario, telemetry data from machinery might show that a machine will fail within two hours. A worker would then receive an alert prompting them to reallocate capacity from that line to others, if possible, to allow for necessary maintenance. Alternatively, in a product development scenario, we might be able to design and test a product virtually. This will help manufacturers create the right product to the correct specifications the first time around, thereby reducing time to market.

As the right analytics become available in 2030 and beyond, manufacturers will gain the real-time insights to deploy end-to-end automation using autonomous tooling. Automation may extend even to the point of delivery, potentially with no human intervention. This data-driven automation will create additional value through improved efficiency and shorter time to revenue generation.

Unlocking Future Manufacturing Success for Enhanced Plant Operations

Currently, we are observing advantages in various aspects of plant operations such as preventive maintenance and quality improvement. For example, vision robotics is already providing assessment data at all stages of manufacturing, from raw materials to finished products. Moving toward 2030, we see manufacturers that start or continue to follow data and analytics best practices gaining significant benefits. These benefits will be in part due to the advantages of an expanded role for AI and the benefits it can provide, enabled by continuously improving data quality. In combination with the Internet of Things (IoT), AI will have the potential to help manufacturers build factories that are more agile, transform their workforce, make significant progress against sustainability objectives, improve security and compliance, and more. Again, this is contingent on establishing the right data standards and practices.

Building Technology Ecosystems that Unleash Data’s Power

Realizing the potential of data as we are discussing it here will not require any singular technology, but rather an ecosystem of solutions working in concert.

Even today, when we consider the data stack, we emphasize cloud-native data solutions that shift away from monolithic platforms towards an ecosystem of best-in-breed, loosely coupled and high-use technologies and tools.

Data in 2030 will similarly require an array of elements. These range from IoT sensors on machines — many of our clients already have such systems in place or are testing them — to the ability to centralize this data and provide global visibility to it. To cite just one example, manufacturers will increasingly implement machine vision, which involves converting video into data for analysis across a wide variety of use cases from quality inspection to safety compliance.

Finding Future Success by Navigating Data’s Complexities to Overcoming Storage, Reliability, and Analytics Challenges

Data storage and reliability challenges I alluded earlier to the need for data centralization. Many of our clients have already conducted pilot projects or have implemented some of the technologies previously discussed in various plants. But most have yet to successfully centralize their data for global visibility. At the heart of this issue will be the need for investment as, historically speaking, manufacturers have not made the significant investments in their data ecosystem needed to resolve this key dependency.

On the flip side, despite the importance of data centralization, there are cases where manufacturers will need to balance their need for centralization versus a need for rapid analysis and action. In such cases, the challenge will be how to embed intelligence into a local system and thereby enable machine-learning-based decision-making at the edge.

Both challenges will be pervasive. Consider a global manufacturer or a multi-site operator aiming to achieve visibility across all their operations. They may have plants that are almost entirely automated with minimal human intervention and others that have a larger human presence. Both environments present the need for clear visibility into each plant’s operations via data, so the different plants might all require a sort of “control room” view. The first step to enabling that view is to store the data on which to base the view. The data may reside in a centralized location or at defined edge locations.

The next step is to ensure the data’s reliability, whether the data is in storage or in transit. This phase involves a data management component that underpins everything before proceeding to the analytical stage.

Data analytics challenges

After overcoming data storage and reliability challenges, manufacturers can shift to the challenge of data analytics. For most manufacturers, the data management in place today is not at a level where they can trust the data without question.

At the same time, there are potential solutions to these challenges from which two separate elements come to mind. First, from a data management perspective, manufacturers could use machine learning algorithms to conduct quality checks and make recommendations on how to master the data. This process is crucial to effective analytics.

The second element pertains to both the techniques of analysis and the human factor involved. Often, achieving success with analytics depends on clearly defining the question we are trying to answer and the action we aim to take. When discussing data and analytics, our shop floor personnel may say they lack the needed technical knowledge. But the reality is, they do not need to understand all the complexities of data. Instead, as data professionals or leaders, we should focus on the actions they take and the decisions they must make in their roles, and develop analytics capabilities accordingly.

Traditional data analytics versus decision-based analytics

Anticipating the evolved and central role the workforce will play in manufacturing in 2030, decision-driven analytics is about determining the necessary actions the workforce must take to create value in a business. First, we need to ask the right questions to guide these actions. We then identify metrics or measures to establish decision points and gauge results. Lastly, we must determine the type of data needed to create those metrics and measures.

The decision-driven approach to analytics can be simplified to four elements: actions, questions, metrics and data. The goal is to understand the actions that create value in a business, then enable these with data strategies and analytics that also yield value.

Fostering Data Literacy and Collaboration to Bridge the Gap Between IT and Business for Action-Oriented Analytics

From a technologist’s perspective, it is essential to understand what is happening in the business and what actions or decisions we are informing or enabling with the technologies we are deploying. As such, IT organizations must enhance their engagement with the business. Simultaneously, business actors might need to upskill their data comprehension.

This does not mean that all business actors must learn to write SQL queries and Python scripts or be up to speed on the latest data science modeling techniques. Rather, it means they must have the requisite level of data literacy to understand the actions they take in their business and how data could help them make better decisions. Approaching it from this angle will help manufacturers find success, in part because business teams and IT organizations are thinking similarly and working towards achieving specific business outcomes.

Infographic. Aspirent, an NTT DATA Company.

To bridge the gap between IT and business teams, we have found success using a structured Decision Architecture framework focused on the following critical topics:

1. What actions do we have at our disposal to drive value for the business?
2. What questions do we need to answer in order to decide which actions to take?
3. What metrics will help us answer those questions?
4. What data do we need to construct those metrics?

This decision-driven framework offers manufacturers a more targeted, action-oriented approach to data and analytics that enables the creation of specific business cases and a clear path to unlocking their data’s potential.

Empowering Manufacturing Excellence by Navigating Data-Driven Insights for Collaborative Success and Outcomes

Ultimately, data analysis and subsequent actions depend on the workforce. It is essential to ensure that we provide data in a format that the workforce can use effectively. Similarly, the speed and effectiveness with which currently top-of-mind technologies can grow is constrained by the need for data that is accurate, complete, reliable, relevant and timely. Data and analytics reside at the core of high-value business outcomes — whether driven by the workforce, AI or combinations thereof. Potential outcomes range from creating more resilient supply chains to accelerating innovation and delivering new services to engaging customers in new ways.

In short, although we have covered a lot of ground here, my single most important piece of advice is to create and maintain a focus on driving collaboration at the intersection of IT and business teams with a focus on the business context and outcomes of data usage. By doing so, manufacturers will be able to increase the business value that data solutions create and avoid complications preventing the realization of that value.

If this data discussion has sparked your interest, we invite you to check out our infographic “Innovate Confidently with Data-Driven Decisions.” It features relevant data from the 2023 NTT DATA Innovation Index.

About the Author
John Petrusick is a Data and Analytics Managing Director at NTT DATA, serving as the Manufacturing Industry Lead. In his role he advises manufacturers on how best to leverage their data to create business value-driven, actionable, and analytic insights to increase efficiency, sustainability, and competitiveness. He is passionate about helping clients solve their data challenges, specifically in data governance, architecture, engineering and integration, advanced analytics and business intelligence, and intelligent automation, as well as helping them implement strategies to drive digital transformation.

Extrapolating from the results of NTT DATA’s recent research report, Innovation Index: Shifting from Disruption to Growth, Kim Curley, NTT DATA’s Digital Evolution Leader and Vice President of People & Organization, shares her insights about the future of the manufacturing workforce in the first of our Manufacturing in 2030: Crystal Ball series.

Picture this: the year is 2030. Across the U.S., the manufacturing workforce has evolved into a symphony of humans and machines working in harmony. This dramatic transformation has made the workforce a technology-enabled powerhouse. Artificial intelligence, robotics, and virtual reality lead the charge, transforming the factory floor into a buzzing and orchestrated hub of activity.

Workers use advanced technologies to perform their tasks more efficiently and safely, collaborating seamlessly with their robotic counterparts. The shop floor thrums with the smooth whirr of precise machinery and the hum of advanced systems. Augmented reality displays guide workers through complex tasks. And to make this possible, the workforce has become a melting pot of diverse talents and skills that is continuously learning and developing

It is a rosy view of the future, but realistic based on current data and trends that illuminate a path to this future state.

Changing Skillsets for 2030

The 2030 manufacturing workforce will be doing things quite differently than they do today. Four in 10 manufacturers have already begun leveraging augmented or virtual reality devices, and that will only increase. That usage will expand to support workforce instruction and training. One example of this could be teaching workers to switch over a production line. Providing that training in a virtual reality environment would mean the company doesn’t need to shut down real-world production.

As the usage of advanced technologies rapidly expands, the skillsets of the manufacturing workforce must expand, too. In addition to physical technical skills, workers will need to strengthen their understanding of technologies as well as problem solving, critical thinking and leadership skills. To build that workforce over time, you must start by expanding the skillsets of managers and leaders now. Starting today is critical to being successful in 2030, indeed, 39% of manufacturers report that they have already increased their investment in leadership development.

A More Resilient Enterprise

The evolved workforce will enable significant benefits. The big one is resilience. An augmented workforce better enables the company to respond to changes in the marketplace. With such a workforce, companies can train faster, reset the lines, and change worker skillsets more rapidly. Paving the way for this evolution, upskilling and reskilling programs for new and/or existing workers are underway today at 54% of manufacturers. More broadly, making your workforce future-ready entails steps such as improving safety, productivity and talent; incentivizing workforce agility; teaching new leadership skills; engaging the workforce; and maintaining a future-focused mindset. We’ll look at each in turn.

Improving Safety, Productivity, and Talent

The NTT DATA 2023 Innovation Index survey showed that businesses are increasingly addressing potential disruptions in strategic plans because these events present opportunities for those businesses that can act with resilience and speed.

So, it is not surprising that manufacturers are placing increased importance on their workforce and the workforce’s ability to adapt and move quickly. The more connected and engaged the workforce is, the better it can shift and flex when the unexpected happens.

Three vital areas come immediately to mind when considering how these coming changes address the problems facing today’s manufacturing industry. The first is safety. As noted earlier, one of the best commercial applications of virtual technologies is training. Training workers to complete physically demanding or potentially hazardous tasks in a completely safe environment — without the cost of physically constructing such simulated environments — is a game changer. Fully 77% of manufacturers report plans to use these technologies within the next two years. That’s well ahead of the cross-industry average of 64%. Using these tools for ongoing training programs helps ensure that workers know exactly what they need to do and how to do it. And it does that while protecting their safety and the safety of the products they manufacture.

A second area of lift is in productivity — specifically, decreasing production errors and downtime. The proliferation of sensors enabled by 5G, advanced computing and machine learning technologies gives us better insights than ever before. These insights enable us to better plan and use scarce resources, another game changer.

The third area is talent. Emerging technologies in the factory call for a new-collar workforce that understands both manufacturing and Manufacturing 4.0. As the industry increases its use of and innovation with advanced technologies, more visionaries will be attracted to manufacturing. At the same time, advocacy initiatives like the Manufacturing Institute’s Creators Wanted program help spread information about this shift in manufacturing work so the next generation of young talent is aware of these opportunities to work with technology. This shift in the reputation of the industry overall can help bring the best and the brightest talent into manufacturing. The workforce will transform from the outside in as it changes from the inside as well.

Incentivizing Workforce Agility

For people and technology to align more closely, it will be important that the workforce sees the benefits of change. Many additional technologies come into play with a technology-enabled workforce. Remote collaboration tools and digital twins can allow manufacturers to assess and optimize processes virtually. These tools may require their users to have specific skills, such as a familiarity with the way data is used to create digital twins, to realize their full value. An agile and augmented workforce will be prepared with those skills or will be readily able to develop them.

The heightened importance of the workforce will call for momentous changes. Historically, manufacturing has considered the human as a component of the manufacturing line. The concept of human capital itself came from the manufacturing industry. Shifting our thinking changes how we develop people and give them desirable career progressions. It also impacts how we pay and incentivize people.

Adopting practices for compensation, rewards, and training from other industries to attract and keep the best and brightest can be beneficial. In high tech, for example, companies such as Google, Apple, and Facebook prioritize employee development and learning opportunities. They offer training programs, mentorships, and career advancement pathways. Some 84% of manufacturers have at least piloted investments in training for employees in areas such as factoring customer behavior into decision-making. To date, though, just three in 10 manufacturers report adjusting total rewards packages to increase their competitiveness when searching for new talent or retaining existing workers.

New Leadership Skills

The path to a future-ready workforce will include overcoming hurdles. One of the biggest is the need for a radically new style of leadership. Leaders in-house today often have massive institutional knowledge and a long history of viewing the workforce in a particular way. Having such leadership has often led to success. But going forward, that will need to change. That is a challenge. You have to teach the most senior leaders to see the workforce — both its potential and its needs—differently. But even incremental change comes slowly. Presently, for example, only 13% of manufacturers report being highly effective at delivering on worker-centric policies such as flexible working options and schedules.

Certainly, you cannot change a whole company’s culture unless you’re working from the top down as well as the bottom up. But once you do, you are positioned to develop and sustain a culture of innovation, experimentation, and resilience.

Engaging the Workforce

The workforce can and should be an active part of its own evolution. First, engage with your workforce – truly engage. Understand who your people are and what is important to them. Find ways to connect them to your company’s purpose and the impact you want to have on the world around you. The stronger those connections are, the more powerful a force your workforce can be in driving the transformation you’ll need over the coming years.

Second, expose more of the workforce to technologies and how these tools can help them and the business. Manufacturers are slightly behind the cross-industry average at the use of updated tools and technologies, with only 29% considering it important to employee satisfaction and engagement. Our Innovation Index shows that the best ideas to advance the business most often come from the individuals who already know the most — your workforce.

A Future-Focused Mindset

Mindset is everything. Without a growth mindset at every level of your organization, you will struggle to accomplish a transformation that will drive success between now and 2030. This mindset must be pervasive, encompassing everything from policies to hiring practices, investment decisions and the employee value proposition you put into the marketplace. When you embrace a mindset that says we are going to try new things and grow, and your workforce embodies it every day, you will create a resilient organization that is comfortable pushing boundaries to create the future.

Learn more about NTT DATA

About the author

Kim Curley is Digital Evolution Leader and Vice President of People & Organization at NTT DATA.

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AI promises to foster a mutually augmented human-machine relationship, making the workforce more productive and strengthening manufacturing capability.

Artificial intelligence is already becoming a commodity tool that is permeating our lives. If you have used Google maps, Apple maps, or shopped on Amazon, then you have already been touched by AI.

Driven by Data

AI is essentially the simulation of human intelligence using computers. Machine learning (ML) is a subset of AI that is used to automatically learn from data without being explicitly programmed. The two key words here are simulation and data. While we’ve all heard about the great promise of AI, it is still a simulation of human intelligence. Right now, human intelligence is superior to artificial intelligence in many sectors, and certainly in manufacturing. Part of the reasoning here, is that AI has yet to be trained in the vast field of manufacturing. Such training requires data. One thing that we do know from manufacturing is that we have significant levels of automation, and that automation has serious amounts of sensors and sensing capabilities on board. So, while there is still much to be learned about AI in manufacturing, we do have substantial amounts of data for that training.

 Understanding the Opportunity

Many initial forays in using AI in manufacturing have been tried. One approach is to take all of the data coming from manufacturing operations and feed them into commercially available AI algorithms to find patterns linking various elements of the manufacturing process to the outcomes (e.g., quality, process health, and the tuning of operations). Many of these attempts are what one would call “black box”, in that they are basically just throwing the data at AI systems to see what happens. This approach has had some limited success. However, more successful approaches have used data with models that are well established but augmented by AI. Augmented is the key word here. An AI augmented model approach uses models based on what we know about our manufacturing processes and supply chains and augments those models to yield more accurate and refined results.

For example, new generation design (CAD) tools employ AI to help design components based on variables such as the load requirements on a part, while minimizing its weight. The approach is generally called generative design, and it creates some very complex structures. One might think that such complex parts can easily be 3D printed – just hit the print button! – as we can now 3D print almost any geometry. However, a person with significant experience in 3D printing will tell you that this kind of print button does not exist and there remain numerous limitations on 3D printed parts. A part generated by an AI design alone may not, in fact, be printable. However, an expert who understands both the goal and the limitations can work with the AI system to generate a lightweight part that can effectively support the required load while still being printable. This is where artificial intelligence works together with real intelligence. The AI is augmenting the human designer, and the human designer is augmenting the AI in this instance. The key is that both are working together.

 Improving the Models

Today, we are just starting to take our data and augment our models to help us better understand our processes. Within a few years, we will have improved models that better utilize sensor data to help us predict the future health of our processes. For example, higher end machine tool manufacturers are starting to place more sensors, such as accelerometers, on their spindles. They train their AI algorithms to understand the sensor output when the spindle is healthy, and then have the algorithms use that knowledge to determine if the spindle has developed a problem. Furthermore, the algorithms can often determine a pending problem, and when it is going to happen. So now, a plant might shut a machine down for maintenance in advance of a breakdown. Thus, repair might happen during the weekend, when the machine is scheduled for maintenance, as opposed to waiting for it to break at an inopportune time such as in the middle of a shift.

“AI has yet to be trained in the vast field of manufacturing. Such training requires data. While there is still much to be learned about AI in manufacturing, we do have substantial amounts of data for that training.”

Such applications of AI will not be a silver bullet. The interpolation/extrapolation limitations of AI will still be in place. That is to say, there is a good chance that AI will not do well outside the scenarios that generated its original training data. So, if a completely new problem or situation is presented to AI, its response can be highly unpredictable. Therefore, we will have to understand its limits. For example, there is no algorithm that can accurately predict when a spindle will fail, until the spindle starts to fail. Statistics can be used to estimate failure (for example, based on experience the spindle may be expected to last so many hours of operation), but until the actual failure starts to occur, one cannot use AI, or any other techniques, to precisely predict that failure. For example, statistically speaking, the spindle might be expected to last for 10,000 hours of operation. However, if a severe crash occurs, that life expectancy might be substantially shortened. That being said, if crash data are fed into AI models, we might get a better feel for how much time was taken off the spindle life due to the severity of the crash, so there is another instance where we can augment our spindle life model.

Augmenting the Human Workforce

Just as with automation, people often express concerns about AI replacing humans in manufacturing jobs. Certainly, we will not see the Skynet AI scenario from the Terminator movie series that is in total control. What we will see is that the AI will augment the human workforce making it more valuable and effective. So once again, rather than artificial intelligence, we will see augmented intelligence. Just as with the earlier generative design example, we can use AI to help the human workforce, and the human workforce can be used to train AI.

Mixed Reality AI

One great technology advancement that is presently coming on-line and will be fully integrated into manufacturing operations by 2030, is mixed reality, which is a blending of the digital and physical worlds. For example, in a plant many workers may have to wear safety glasses, so why not make them augmented reality (AR) goggles? Such goggles could ensure that an operator follows the right procedures and adheres to all the required safety protocols. It could also ensure the correct insertion of all of parts into an assembly. Such an approach is the best of both the human and automation worlds.

Automated assembly makes sense in a highly controlled environment when thousands or tens of thousands of assemblies are being made and the components of the assembly are always in the same location and orientation so that automated units such as robots know exactly where and how to put up these parts. But if a part is backwards or falls over for some reason, the robot may have great difficulty picking it up. A human, however, is very adept at determining a part’s location and orientation and picking it up to put into an assembly correctly. Furthermore, AR goggles could ensure that the person picks up the right components and puts them into the right locations. An added benefit to this, is that a digital passport can then be generated for the assembly that records the correct assembly procedure documenting every process step ensuring the assembly’s quality.

“Augmented is the key word here. An AI augmented model approach uses models based on what we know about our manufacturing processes and supply chains and augments those models to yield more accurate and refined results.”

Such a capability can also be used to help to train new workers in a facility and reduce their errors substantially, especially if the AR is used to train the AI by learning directly from the actions and decisions of experienced workers. This does not eliminate the human from the job, as a human is still necessary to physically execute the task. But AI could learn the most effective manner to execute a task and then guide a novice through a similar procedure using AR. All this can be done while building the product’s digital passport, conducting time and motion studies, and ensuring that all safety protocols are followed. It is truly an approach that augments and adds value to the workforce.

Privacy Implications

If we are recording every move and motion of the workforce, this does raise some privacy concerns which will need to be addressed in the years ahead. We will need to understand what we are learning and recording from the workforce and ensure that their data are protected accordingly. This is not unlike Google maps. We share our location with Google, and in turn, Google provides us with directions to where we want to go. Of course one major concern here is that Google might use our location data to track us and provide that information to third parties. The same is true in manufacturing. How do we ensure privacy among workers and businesses? This does open up a can of worms as, right now, different geographic regions (e.g., EU, U.S., China) have different regulations regarding personal data and its privacy. So, we will certainly have to address these issues which could become increasingly complex for multinational companies over the next few years.

Furthermore, the ability to share production data from one production facility to other facilities, or from one machine tool to another, is going to become important as lessons learned in one location could be equally as valuable to others at different locations. This highlights the value of data, but it begs the question, why would factory owners open their cyber doors to others and share their data? Clearly, new business and privacy models and rules must be developed to make it both worthwhile and safe for companies, and industries, to share some of their data.

Looking Ahead

So, I believe that by 2030 we are going to see AI capabilities permeate throughout manufacturing. We are also going to see it really take off in mixed reality settings that will increasingly look like something out of a science fiction movie. This will result in a safer, more efficient, and higher value-added manufacturing ecosystem. But it will also result in some very different business and socio-economic models that will change the way manufactures operate, think, and profit in the years ahead.  M

About the author:
Professor Thomas R. Kurfess is Executive Director at the Georgia Tech Manufacturing Institute, Professor and HUSCO/Ramirez Distinguished Chair in Fluid Power and Motion Control at the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology, and a member of the MLC’s Board of Governors.

When toddlers learn to stack blocks, they learn by trial and error — often with immediate feedback from a parent or other adult. It is a model-free learning process, or reinforcement learning, that does not require them to learn Newton’s equation to figure out how to stack the blocks.

For Dr. Hiroaki Kanokogi, Yokogawa Digital Corporation’s President and CEO, reinforcement learning (RL) in artificial intelligence (AI) has direct uses in a manufacturing environment. In fact, as Kanokogi shared during the Manufacturing Leadership Council’s Manufacturing in 2030 Project: Let’s Talk About AI event, RL was a building block when Yokogawa used AI to autonomously control a Japanese chemical plant for 35 days earlier this year.

In his presentation at Let’s Talk About AI, Kanokogi shared that there are serious challenges to applying RL to real world manufacturing. First, he said, traditional RL takes 1 million to 1 billion trials to go beyond human learning, and second, manufacturers must include safety assurances.

To overcome the first of these challenges, Yokogawa and the Nara Institute of Science and Technology developed scalable reinforcement learning called Factorial Kernel Dynamic Policy Programming (FKDPP) specifically for plant control. FKDPP allows for faster learning (typically in about 30 trials) and robust protection against disturbances. Yokogawa was able to demonstrate that FKDPP can autonomously stabilize water levels in a fundamental three tank level control experiment significantly quicker than traditional proportional-integral-derivative (PID) control.

At Let’s Talk About AI, Kanokogi shared four videos that chronicled FKDPP’s iterative attempts to stabilize the water. In the first iteration, AI does not know anything yet, so when the valve is opened the water level goes all the way up. In the 20th iteration, AI can control the water in a somewhat stable manner, but it varies and resembles a human’s performance on the task. For the 25^th iteration, AI learns how to regulate the variation. By the 30^th iteration, the FKDPP perfects the process. Kanokogi pointed out that this final iteration demonstrated that once AI finds a good way, optimization of this process is AI’s strength.

For the second challenge around safety assurance, Yokogawa was able to prove AI can satisfy this need during this year’s 35-day autonomous factory operation. The company first built a good simulation model by using domain knowledge in a digital twin so the AI could learn. Step two called for simulation and evaluation using both past and live data. Finally, the company ensured safety and control in the actual plant using Yokogawa’s integrated process control system, CENTUM™ VP DCS.

For Yokogawa and its autonomous operation, Kanokogi reported that he and his team continue to look at problems in the factory where AI can be applied. While the first-of-its-kind, 35-day automation demonstration is truly impressive, he sees manufacturing working in an autonomous plan-do-check-act (PDCA) loop by 2030. This loop will run continuously, and AI will help the plant improve itself. While there is no need for human intervention during this loop, Kanokogi pointed out that AI cannot add new sensors or integrate new technologies, so human experts will maintain a defined role in manufacturing.

Like a toddler with blocks, autonomous factory operation might be in its nascent years, but with the help of AI and Yokogawa’s FKDPP technology, maturation by 2030 is possible.

Manufacturing in 2030 Project: Let’s Talk About AI was held Dec. 7-8, 2022 in Nashville, Tenn. The event was part of MLC’s Manufacturing in 2030 Project.